For core business automations, agentic AIs that "guess" are expensive and unreliable. A superior approach uses tools that convert natural language into deterministic, code-like workflows, which run consistently and use AI only when necessary.

LLMs are unsuitable for critical business functions like pricing optimization. These tasks require deterministic, cheap, and accurate outputs—three criteria that current LLMs fail to meet, making them a poor fit for enterprise decision automation.

Web3 enables "programmable money" through smart contracts, which act like automated referees. For channel incentives like MDF, rules are coded into a smart contract. When a partner meets a target, payment in a stablecoin is released instantly and globally, eliminating delays, bureaucracy, and cash flow issues for partners.

A significant portion of B2B contracts will soon be negotiated and executed by autonomous AI agents. This shift will create an entirely new class of disputes when agents err, necessitating automated, potentially on-chain, systems to resolve conflicts efficiently without human intervention.

For critical enterprise functions like financial modeling, 99.9% accuracy from a probabilistic LLM is unacceptable. Platforms like Salesforce's Agent Force 360 solve this by layering deterministic logic and guardrails on top of the AI, ensuring compliance and preventing costly errors where even a 0.1% failure rate is too high.

Building reliable AI agents for finance, where accuracy is critical, requires moving beyond pure LLMs. Xero uses a hybrid system combining LLM-driven workflows with programmatic code and deep domain knowledge to ensure control and reliability that LLMs inherently lack.

Relying solely on natural language prompts like 'always do this' is unreliable for enterprise AI. LLMs struggle with deterministic logic. Salesforce developed 'AgentForce Script,' a dedicated language to enforce rules and ensure consistent, repeatable performance for critical business workflows, blending it with LLM reasoning.

For an LLM's output to be useful in a software system, it cannot be treated as ambiguous text. It must be forced through a "hard boundary"—a strict schema or contract—that constrains, validates, and types the data, making it observable and safe for downstream services to trust and consume.

To deploy LLMs in high-stakes environments like finance, combine them with deterministic checks. For example, use a traditional algorithm to calculate cash flow and only surface the LLM's answer if it falls within an acceptable range. This prevents hallucinations and ensures reliability.